Finish 2.3.1 exercise 1

CV-App/algorithms/invis_cloak.py

@@ -63,7 +63,7 @@ class InvisCloak (Algorithm):
 
             # Stores the current image to data folder
             cv2.imwrite(f"results/{datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}_original_image.png",
-                        self.picture_buffer[4])
+                        self.picture_buffer[self.n - 1])
 
             # Create RGB histogram
             self._221_RGB(self.middle_value_picture)
@@ -71,6 +71,14 @@ class InvisCloak (Algorithm):
             # Create HSV histogram
             self._222_HSV(self.middle_value_picture)
 
+            # Get binary mask and write it to file
+            binary_mask = self._23_SegmentUndBildmodifizierung(self.middle_value_picture, True)
+            cv2.imwrite(f"results/{datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}_binary_mask.png",
+                        binary_mask)
+        elif event == cv2.EVENT_MBUTTONUP:
+            # Save current image as background
+            cv2.imwrite(f"results/background.png", self.picture_buffer[self.n - 1])
+
 
     def _plotNoise(self, img, name:str):
         height, width = np.array(img.shape[:2])
@@ -136,14 +144,16 @@ class InvisCloak (Algorithm):
             Hier steht Ihr Code zu Aufgabe 2.2.1 (RGB)
             - Histogrammberechnung und Analyse
         """
-        # Names of the color spectrums
+        # Names of the colors in histogram
         channels = ["b", "g", "r"]
 
+        # Calc histogram
         for index, channel_name in enumerate(channels):
             hist = cv2.calcHist([img], [index], None, [256], [0, 256])
             plt.plot(hist, color=channel_name)
             plt.xlim([0, 256])
 
+        # Save histogram, clear cache
         plt.savefig(f"results/{datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}_histogram_{colorspectrum}.png")
         plt.clf()
 
@@ -153,14 +163,37 @@ class InvisCloak (Algorithm):
             Hier steht Ihr Code zu Aufgabe 2.2.2 (HSV)
             - Histogrammberechnung und Analyse im HSV-Raum
         """
+        # Convert image to hsv, call _221_RGB to create histogram
         self._221_RGB(cv2.cvtColor(img, cv2.COLOR_BGR2HSV), "hsv")
 
 
-    def _23_SegmentUndBildmodifizierung (self, img):
+    def _23_SegmentUndBildmodifizierung (self, img, save_binary_mask = False):
         """
             Hier steht Ihr Code zu Aufgabe 2.3.1 (StatischesSchwellwertverfahren)
             - Binärmaske erstellen
         """
+        # 0 = blue, 1 = green, 2 = red
+        selected_color_channel = 2
+
+        # Color threshold values for color deletion
+        lower_bound, upper_bound = 100, 255
+
+        # Creating binary mask
+        binary_mask = (lower_bound < img[:, :, selected_color_channel]) * (img[:, :, selected_color_channel] < upper_bound)
+
+        # Store binary mask to results folder
+        if save_binary_mask:
+            cv2.imwrite(f"results/{datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}_binary_mask.png",
+                        binary_mask * 256)
+
+        try:
+            # Get background image
+            background = cv2.imread("results/background.png")
+
+            # Apply mask to image
+            img[binary_mask] = background[binary_mask]
+        except:
+            print("No background image")
 
 
         """